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chart</title> <meta name="description" content="Search results for: ASM chart"> <meta name="keywords" content="ASM chart"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none navbar-toggler ml-auto" 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id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="ASM chart"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 255</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: ASM chart</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">255</span> The Variable Sampling Interval Xbar Chart versus the Double Sampling Xbar Chart</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Michael%20B.%20C.%20Khoo">Michael B. C. Khoo</a>, <a href="https://publications.waset.org/abstracts/search?q=J.%20L.%20Khoo"> J. L. Khoo</a>, <a href="https://publications.waset.org/abstracts/search?q=W.%20C.%20Yeong"> W. C. Yeong</a>, <a href="https://publications.waset.org/abstracts/search?q=W.%20L.%20Teoh"> W. L. Teoh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Shewhart Xbar control chart is a useful process monitoring tool in manufacturing industries to detect the presence of assignable causes. However, it is insensitive in detecting small process shifts. To circumvent this problem, adaptive control charts are suggested. An adaptive chart enables at least one of the chart鈥檚 parameters to be adjusted to increase the chart鈥檚 sensitivity. Two common adaptive charts that exist in the literature are the double sampling (DS) Xbar and variable sampling interval (VSI) Xbar charts. This paper compares the performances of the DS and VSI Xbar charts, based on the average time to signal (ATS) criterion. The ATS profiles of the DS Xbar and VSI Xbar charts are obtained using the Mathematica and Statistical Analysis System (SAS) programs, respectively. The results show that the VSI Xbar chart is generally superior to the DS Xbar chart. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=adaptive%20charts" title="adaptive charts">adaptive charts</a>, <a href="https://publications.waset.org/abstracts/search?q=average%20time%20to%20signal" title=" average time to signal"> average time to signal</a>, <a href="https://publications.waset.org/abstracts/search?q=double%20sampling" title=" double sampling"> double sampling</a>, <a href="https://publications.waset.org/abstracts/search?q=charts" title=" charts"> charts</a>, <a href="https://publications.waset.org/abstracts/search?q=variable%20sampling%20interval" title=" variable sampling interval"> variable sampling interval</a> </p> <a href="https://publications.waset.org/abstracts/45295/the-variable-sampling-interval-xbar-chart-versus-the-double-sampling-xbar-chart" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/45295.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">286</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">254</span> An EWMA P-Chart Based on Improved Square Root Transformation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Saowanit%20Sukparungsee">Saowanit Sukparungsee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Generally, the traditional Shewhart p chart has been developed by for charting the binomial data. This chart has been developed using the normal approximation with condition as low defect level and the small to moderate sample size. In real applications, however, are away from these assumptions due to skewness in the exact distribution. In this paper, a modified Exponentially Weighted Moving Average (EWMA) control chat for detecting a change in binomial data by improving square root transformations, namely ISRT p EWMA control chart. The numerical results show that ISRT p EWMA chart is superior to ISRT p chart for small to moderate shifts, otherwise, the latter is better for large shifts. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=number%20of%20defects" title="number of defects">number of defects</a>, <a href="https://publications.waset.org/abstracts/search?q=exponentially%20weighted%20moving%20average" title=" exponentially weighted moving average"> exponentially weighted moving average</a>, <a href="https://publications.waset.org/abstracts/search?q=average%20run%20length" title=" average run length"> average run length</a>, <a href="https://publications.waset.org/abstracts/search?q=square%20root%20transformations" title=" square root transformations"> square root transformations</a> </p> <a href="https://publications.waset.org/abstracts/10613/an-ewma-p-chart-based-on-improved-square-root-transformation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/10613.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">440</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">253</span> Statistical Design of Synthetic VP X-bar Control Chat Using Markov Chain Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ali%20Akbar%20Heydari">Ali Akbar Heydari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Control charts are an important tool of statistical quality control. Thesecharts are used to detect and eliminate unwanted special causes of variation that occurred during aperiod of time. The design and operation of control charts require the determination of three design parameters: the sample size (n), the sampling interval (h), and the width coefficient of control limits (k). Thevariable parameters (VP) x-bar controlchart is the x-barchart in which all the design parameters vary between twovalues. These values are a function of the most recent process information. In fact, in the VP x-bar chart, the position of each sample point on the chart establishes the size of the next sample and the timeof its sampling. The synthetic x-barcontrol chartwhich integrates the x-bar chart and the conforming run length (CRL) chart, provides significant improvement in terms of detection power over the basic x-bar chart for all levels of mean shifts. In this paper, we introduce the syntheticVP x-bar control chart for monitoring changes in the process mean. To determine the design parameters, we used a statistical design based on the minimum out of control average run length (ARL) criteria. The optimal chart parameters of the proposed chart are obtained using the Markov chain approach. A numerical example is also done to show the performance of the proposed chart and comparing it with the other control charts. The results show that our proposed syntheticVP x-bar controlchart perform better than the synthetic x-bar controlchart for all shift parameter values. Also, the syntheticVP x-bar controlchart perform better than the VP x-bar control chart for the moderate or large shift parameter values. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=control%20chart" title="control chart">control chart</a>, <a href="https://publications.waset.org/abstracts/search?q=markov%20chain%20approach" title=" markov chain approach"> markov chain approach</a>, <a href="https://publications.waset.org/abstracts/search?q=statistical%20design" title=" statistical design"> statistical design</a>, <a href="https://publications.waset.org/abstracts/search?q=synthetic" title=" synthetic"> synthetic</a>, <a href="https://publications.waset.org/abstracts/search?q=variable%20parameter" title=" variable parameter"> variable parameter</a> </p> <a href="https://publications.waset.org/abstracts/146094/statistical-design-of-synthetic-vp-x-bar-control-chat-using-markov-chain-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/146094.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">154</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">252</span> Optimal Design for SARMA(P,Q)L Process of EWMA Control Chart</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yupaporn%20Areepong">Yupaporn Areepong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The main goal of this paper is to study Statistical Process Control (SPC) with Exponentially Weighted Moving Average (EWMA) control chart when observations are serially-correlated. The characteristic of control chart is Average Run Length (ARL) which is the average number of samples taken before an action signal is given. Ideally, an acceptable ARL of in-control process should be enough large, so-called (ARL0). Otherwise it should be small when the process is out-of-control, so-called Average of Delay Time (ARL1) or a mean of true alarm. We find explicit formulas of ARL for EWMA control chart for Seasonal Autoregressive and Moving Average processes (SARMA) with Exponential white noise. The results of ARL obtained from explicit formula and Integral equation are in good agreement. In particular, this formulas for evaluating (ARL0) and (ARL1) be able to get a set of optimal parameters which depend on smoothing parameter (位) and width of control limit (H) for designing EWMA chart with minimum of (ARL1). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=average%20run%20length" title="average run length">average run length</a>, <a href="https://publications.waset.org/abstracts/search?q=optimal%20parameters" title=" optimal parameters"> optimal parameters</a>, <a href="https://publications.waset.org/abstracts/search?q=exponentially%20weighted%20moving%20average%20%28EWMA%29" title=" exponentially weighted moving average (EWMA)"> exponentially weighted moving average (EWMA)</a>, <a href="https://publications.waset.org/abstracts/search?q=control%20chart" title=" control chart"> control chart</a> </p> <a href="https://publications.waset.org/abstracts/10653/optimal-design-for-sarmapql-process-of-ewma-control-chart" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/10653.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">560</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">251</span> Application of Hyperbinomial Distribution in Developing a Modified p-Chart</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shourav%20Ahmed">Shourav Ahmed</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Gulam%20Kibria"> M. Gulam Kibria</a>, <a href="https://publications.waset.org/abstracts/search?q=Kais%20Zaman"> Kais Zaman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Control charts graphically verify variation in quality parameters. Attribute type control charts deal with quality parameters that can only hold two states, e.g., good or bad, yes or no, etc. At present, p-control chart is most commonly used to deal with attribute type data. In construction of p-control chart using binomial distribution, the value of proportion non-conforming must be known or estimated from limited sample information. As the probability distribution of fraction non-conforming (p) is considered in hyperbinomial distribution unlike a constant value in case of binomial distribution, it reduces the risk of false detection. In this study, a statistical control chart is proposed based on hyperbinomial distribution when prior estimate of proportion non-conforming is unavailable and is estimated from limited sample information. We developed the control limits of the proposed modified p-chart using the mean and variance of hyperbinomial distribution. The proposed modified p-chart can also utilize additional sample information when they are available. The study also validates the use of modified p-chart by comparing with the result obtained using cumulative distribution function of hyperbinomial distribution. The study clearly indicates that the use of hyperbinomial distribution in construction of p-control chart yields much accurate estimate of quality parameters than using binomial distribution. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=binomial%20distribution" title="binomial distribution">binomial distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=control%20charts" title=" control charts"> control charts</a>, <a href="https://publications.waset.org/abstracts/search?q=cumulative%20distribution%20function" title=" cumulative distribution function"> cumulative distribution function</a>, <a href="https://publications.waset.org/abstracts/search?q=hyper%20binomial%20distribution" title=" hyper binomial distribution"> hyper binomial distribution</a> </p> <a href="https://publications.waset.org/abstracts/90750/application-of-hyperbinomial-distribution-in-developing-a-modified-p-chart" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/90750.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">279</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">250</span> Optimal Bayesian Chart for Controlling Expected Number of Defects in Production Processes</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=V.%20Makis">V. Makis</a>, <a href="https://publications.waset.org/abstracts/search?q=L.%20Jafari"> L. Jafari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we develop an optimal Bayesian chart to control the expected number of defects per inspection unit in production processes with long production runs. We formulate this control problem in the optimal stopping framework. The objective is to determine the optimal stopping rule minimizing the long-run expected average cost per unit time considering partial information obtained from the process sampling at regular epochs. We prove the optimality of the control limit policy, i.e., the process is stopped and the search for assignable causes is initiated when the posterior probability that the process is out of control exceeds a control limit. An algorithm in the semi-Markov decision process framework is developed to calculate the optimal control limit and the corresponding average cost. Numerical examples are presented to illustrate the developed optimal control chart and to compare it with the traditional u-chart. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20u-chart" title="Bayesian u-chart">Bayesian u-chart</a>, <a href="https://publications.waset.org/abstracts/search?q=economic%20design" title=" economic design"> economic design</a>, <a href="https://publications.waset.org/abstracts/search?q=optimal%20stopping" title=" optimal stopping"> optimal stopping</a>, <a href="https://publications.waset.org/abstracts/search?q=semi-Markov%20decision%20process" title=" semi-Markov decision process"> semi-Markov decision process</a>, <a href="https://publications.waset.org/abstracts/search?q=statistical%20process%20control" title=" statistical process control"> statistical process control</a> </p> <a href="https://publications.waset.org/abstracts/62841/optimal-bayesian-chart-for-controlling-expected-number-of-defects-in-production-processes" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/62841.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">573</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">249</span> Design Data Sorter Circuit Using Insertion Sorting Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hoda%20Abugharsa">Hoda Abugharsa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper we propose to design a sorter circuit using insertion sorting algorithm. The circuit will be designed using Algorithmic State Machines (ASM) method. That means converting the insertion sorting flowchart into an ASM chart. Then the ASM chart will be used to design the sorter circuit and the control unit. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=insert%20sorting%20algorithm" title="insert sorting algorithm">insert sorting algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=ASM%20chart" title=" ASM chart"> ASM chart</a>, <a href="https://publications.waset.org/abstracts/search?q=sorter%20circuit" title=" sorter circuit"> sorter circuit</a>, <a href="https://publications.waset.org/abstracts/search?q=state%20machine" title=" state machine"> state machine</a>, <a href="https://publications.waset.org/abstracts/search?q=control%20unit" title=" control unit"> control unit</a> </p> <a href="https://publications.waset.org/abstracts/5614/design-data-sorter-circuit-using-insertion-sorting-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/5614.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">445</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">248</span> Process Monitoring Based on Parameterless Self-Organizing Map</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Young%20Jae%20Choung">Young Jae Choung</a>, <a href="https://publications.waset.org/abstracts/search?q=Seoung%20Bum%20Kim"> Seoung Bum Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Statistical Process Control (SPC) is a popular technique for process monitoring. A widely used tool in SPC is a control chart, which is used to detect the abnormal status of a process and maintain the controlled status of the process. Traditional control charts, such as Hotelling鈥檚 T2 control chart, are effective techniques to detect abnormal observations and monitor processes. However, many complicated manufacturing systems exhibit nonlinearity because of the different demands of the market. In this case, the unregulated use of a traditional linear modeling approach may not be effective. In reality, many industrial processes contain the nonlinear and time-varying properties because of the fluctuation of process raw materials, slowing shift of the set points, aging of the main process components, seasoning effects, and catalyst deactivation. The use of traditional SPC techniques with time-varying data will degrade the performance of the monitoring scheme. To address these issues, in the present study, we propose a parameterless self-organizing map (PLSOM)-based control chart. The PLSOM-based control chart not only can manage a situation where the distribution or parameter of the target observations changes, but also address the nonlinearity of modern manufacturing systems. The control limits of the proposed PLSOM chart are established by estimating the empirical level of significance on the percentile using a bootstrap method. Experimental results with simulated data and actual process data from a thin-film transistor-liquid crystal display process demonstrated the effectiveness and usefulness of the proposed chart. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=control%20chart" title="control chart">control chart</a>, <a href="https://publications.waset.org/abstracts/search?q=parameter-less%20self-organizing%20map" title=" parameter-less self-organizing map"> parameter-less self-organizing map</a>, <a href="https://publications.waset.org/abstracts/search?q=self-organizing%20map" title=" self-organizing map"> self-organizing map</a>, <a href="https://publications.waset.org/abstracts/search?q=time-varying%20property" title=" time-varying property"> time-varying property</a> </p> <a href="https://publications.waset.org/abstracts/52108/process-monitoring-based-on-parameterless-self-organizing-map" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/52108.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">275</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">247</span> Optimal Bayesian Control of the Proportion of Defectives in a Manufacturing Process</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Viliam%20Makis">Viliam Makis</a>, <a href="https://publications.waset.org/abstracts/search?q=Farnoosh%20Naderkhani"> Farnoosh Naderkhani</a>, <a href="https://publications.waset.org/abstracts/search?q=Leila%20Jafari"> Leila Jafari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we present a model and an algorithm for the calculation of the optimal control limit, average cost, sample size, and the sampling interval for an optimal Bayesian chart to control the proportion of defective items produced using a semi-Markov decision process approach. Traditional p-chart has been widely used for controlling the proportion of defectives in various kinds of production processes for many years. It is well known that traditional non-Bayesian charts are not optimal, but very few optimal Bayesian control charts have been developed in the literature, mostly considering finite horizon. The objective of this paper is to develop a fast computational algorithm to obtain the optimal parameters of a Bayesian p-chart. The decision problem is formulated in the partially observable framework and the developed algorithm is illustrated by a numerical example. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20control%20chart" title="Bayesian control chart">Bayesian control chart</a>, <a href="https://publications.waset.org/abstracts/search?q=semi-Markov%20decision%20process" title=" semi-Markov decision process"> semi-Markov decision process</a>, <a href="https://publications.waset.org/abstracts/search?q=quality%20control" title=" quality control"> quality control</a>, <a href="https://publications.waset.org/abstracts/search?q=partially%20observable%20process" title=" partially observable process"> partially observable process</a> </p> <a href="https://publications.waset.org/abstracts/49751/optimal-bayesian-control-of-the-proportion-of-defectives-in-a-manufacturing-process" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/49751.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">319</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">246</span> EWMA and MEWMA Control Charts for Monitoring Mean and Variance in Industrial Processes</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=L.%20A.%20Toro">L. A. Toro</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20Prieto"> N. Prieto</a>, <a href="https://publications.waset.org/abstracts/search?q=J.%20J.%20Vargas"> J. J. Vargas </a> </p> <p class="card-text"><strong>Abstract:</strong></p> There are many control charts for monitoring mean and variance. Among these, the X y R, X y S, S2 Hotteling and Shewhart control charts, for mentioning some, are widely used for monitoring mean a variance in industrial processes. In particular, the Shewhart charts are based on the information about the process contained in the current observation only and ignore any information given by the entire sequence of points. Moreover, that the Shewhart chart is a control chart without memory. Consequently, Shewhart control charts are found to be less sensitive in detecting smaller shifts, particularly smaller than 1.5 times of the standard deviation. These kind of small shifts are important in many industrial applications. In this study and effective alternative to Shewhart control chart was implemented. In case of univariate process an Exponentially Moving Average (EWMA) control chart was developed and Multivariate Exponentially Moving Average (MEWMA) control chart in case of multivariate process. Both of these charts were based on memory and perform better that Shewhart chart while detecting smaller shifts. In these charts, information the past sample is cumulated up the current sample and then the decision about the process control is taken. The mentioned characteristic of EWMA and MEWMA charts, are of the paramount importance when it is necessary to control industrial process, because it is possible to correct or predict problems in the processes before they come to a dangerous limit. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=control%20charts" title="control charts">control charts</a>, <a href="https://publications.waset.org/abstracts/search?q=multivariate%20exponentially%20moving%20average%20%28MEWMA%29" title=" multivariate exponentially moving average (MEWMA)"> multivariate exponentially moving average (MEWMA)</a>, <a href="https://publications.waset.org/abstracts/search?q=exponentially%20moving%20average%20%28EWMA%29" title=" exponentially moving average (EWMA)"> exponentially moving average (EWMA)</a>, <a href="https://publications.waset.org/abstracts/search?q=industrial%20control%20process" title=" industrial control process"> industrial control process</a> </p> <a href="https://publications.waset.org/abstracts/38377/ewma-and-mewma-control-charts-for-monitoring-mean-and-variance-in-industrial-processes" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/38377.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">355</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">245</span> Economic Design of a Quality Control Chart for the Proportion of Defective Items</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Encarnaci%C3%B3n%20%C3%81lvarez-Verdejo">Encarnaci贸n 脕lvarez-Verdejo</a>, <a href="https://publications.waset.org/abstracts/search?q=Ra%C3%BAl%20Amor-Pulido"> Ra煤l Amor-Pulido</a>, <a href="https://publications.waset.org/abstracts/search?q=Pablo%20J.%20Moya-Fern%C3%A1ndez"> Pablo J. Moya-Fern谩ndez</a>, <a href="https://publications.waset.org/abstracts/search?q=Juan%20F.%20Mu%C3%B1oz-Rosas"> Juan F. Mu帽oz-Rosas</a>, <a href="https://publications.waset.org/abstracts/search?q=Francisco%20J.%20Blanco-Encomienda"> Francisco J. Blanco-Encomienda</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Many companies use the statistical tool named as statistical quality control, and which can have a high cost for the companies interested on these statistical tools. The evaluation of the quality of products and services is an important topic, but the reduction of the cost of the implantation of the statistical quality control also has important benefits for the companies. For this reason, it is important to implement a economic design for the various steps included into the statistical quality control. In this paper, we describe some relevant aspects related to the economic design of a quality control chart for the proportion of defective items. They are very important because the suggested issues can reduce the cost of implementing a quality control chart for the proportion of defective items. Note that the main purpose of this chart is to evaluate and control the proportion of defective items of a production process. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=proportion" title="proportion">proportion</a>, <a href="https://publications.waset.org/abstracts/search?q=type%20I%20error" title=" type I error"> type I error</a>, <a href="https://publications.waset.org/abstracts/search?q=economic%20plan" title=" economic plan"> economic plan</a>, <a href="https://publications.waset.org/abstracts/search?q=distribution%20function" title=" distribution function"> distribution function</a> </p> <a href="https://publications.waset.org/abstracts/42442/economic-design-of-a-quality-control-chart-for-the-proportion-of-defective-items" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/42442.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">443</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">244</span> A Study on the False Alarm Rates of MEWMA and MCUSUM Control Charts When the Parameters Are Estimated</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Umar%20Farouk%20Abbas">Umar Farouk Abbas</a>, <a href="https://publications.waset.org/abstracts/search?q=Danjuma%20Mustapha"> Danjuma Mustapha</a>, <a href="https://publications.waset.org/abstracts/search?q=Hamisu%20Idi"> Hamisu Idi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> It is now a known fact that quality is an important issue in manufacturing industries. A control chart is an integrated and powerful tool in statistical process control (SPC). The mean 碌 and standard deviation 蟽 parameters are estimated. In general, the multivariate exponentially weighted moving average (MEWMA) and multivariate cumulative sum (MCUSUM) are used in the detection of small shifts in joint monitoring of several correlated variables; the charts used information from past data which makes them sensitive to small shifts. The aim of the paper is to compare the performance of Shewhart xbar, MEWMA, and MCUSUM control charts in terms of their false rates when parameters are estimated with autocorrelation. A simulation was conducted in R software to generate the average run length (ARL) values of each of the charts. After the analysis, the results show that a comparison of the false alarm rates of the charts shows that MEWMA chart has lower false alarm rates than the MCUSUM chart at various levels of parameter estimated to the number of ARL0 (in control) values. Also noticed was that the sample size has an advert effect on the false alarm of the control charts. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=average%20run%20length" title="average run length">average run length</a>, <a href="https://publications.waset.org/abstracts/search?q=MCUSUM%20chart" title=" MCUSUM chart"> MCUSUM chart</a>, <a href="https://publications.waset.org/abstracts/search?q=MEWMA%20chart" title=" MEWMA chart"> MEWMA chart</a>, <a href="https://publications.waset.org/abstracts/search?q=false%20alarm%20rate" title=" false alarm rate"> false alarm rate</a>, <a href="https://publications.waset.org/abstracts/search?q=parameter%20estimation" title=" parameter estimation"> parameter estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=simulation" title=" simulation"> simulation</a> </p> <a href="https://publications.waset.org/abstracts/106504/a-study-on-the-false-alarm-rates-of-mewma-and-mcusum-control-charts-when-the-parameters-are-estimated" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/106504.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">222</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">243</span> On the Bootstrap P-Value Method in Identifying out of Control Signals in Multivariate Control Chart</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=O.%20Ikpotokin">O. Ikpotokin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In any production process, every product is aimed to attain a certain standard, but the presence of assignable cause of variability affects our process, thereby leading to low quality of product. The ability to identify and remove this type of variability reduces its overall effect, thereby improving the quality of the product. In case of a univariate control chart signal, it is easy to detect the problem and give a solution since it is related to a single quality characteristic. However, the problems involved in the use of multivariate control chart are the violation of multivariate normal assumption and the difficulty in identifying the quality characteristic(s) that resulted in the out of control signals. The purpose of this paper is to examine the use of non-parametric control chart (the bootstrap approach) for obtaining control limit to overcome the problem of multivariate distributional assumption and the p-value method for detecting out of control signals. Results from a performance study show that the proposed bootstrap method enables the setting of control limit that can enhance the detection of out of control signals when compared, while the p-value method also enhanced in identifying out of control variables. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bootstrap%20control%20limit" title="bootstrap control limit">bootstrap control limit</a>, <a href="https://publications.waset.org/abstracts/search?q=p-value%20method" title=" p-value method"> p-value method</a>, <a href="https://publications.waset.org/abstracts/search?q=out-of-control%20signals" title=" out-of-control signals"> out-of-control signals</a>, <a href="https://publications.waset.org/abstracts/search?q=p-value" title=" p-value"> p-value</a>, <a href="https://publications.waset.org/abstracts/search?q=quality%20characteristics" title=" quality characteristics"> quality characteristics</a> </p> <a href="https://publications.waset.org/abstracts/77853/on-the-bootstrap-p-value-method-in-identifying-out-of-control-signals-in-multivariate-control-chart" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/77853.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">347</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">242</span> Small Text Extraction from Documents and Chart Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rominkumar%20Busa">Rominkumar Busa</a>, <a href="https://publications.waset.org/abstracts/search?q=Shahira%20K.%20C."> Shahira K. C.</a>, <a href="https://publications.waset.org/abstracts/search?q=Lijiya%20A."> Lijiya A.</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Text recognition is an important area in computer vision which deals with detecting and recognising text from an image. The Optical Character Recognition (OCR) is a saturated area these days and with very good text recognition accuracy. However the same OCR methods when applied on text with small font sizes like the text data of chart images, the recognition rate is less than 30%. In this work, aims to extract small text in images using the deep learning model, CRNN with CTC loss. The text recognition accuracy is found to improve by applying image enhancement by super resolution prior to CRNN model. We also observe the text recognition rate further increases by 18% by applying the proposed method, which involves super resolution and character segmentation followed by CRNN with CTC loss. The efficiency of the proposed method shows that further pre-processing on chart image text and other small text images will improve the accuracy further, thereby helping text extraction from chart images. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=small%20text%20extraction" title="small text extraction">small text extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=OCR" title=" OCR"> OCR</a>, <a href="https://publications.waset.org/abstracts/search?q=scene%20text%20recognition" title=" scene text recognition"> scene text recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=CRNN" title=" CRNN"> CRNN</a> </p> <a href="https://publications.waset.org/abstracts/150310/small-text-extraction-from-documents-and-chart-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/150310.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">125</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">241</span> Adaptive Process Monitoring for Time-Varying Situations Using Statistical Learning Algorithms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Seulki%20Lee">Seulki Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Seoung%20Bum%20Kim"> Seoung Bum Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Statistical process control (SPC) is a practical and effective method for quality control. The most important and widely used technique in SPC is a control chart. The main goal of a control chart is to detect any assignable changes that affect the quality output. Most conventional control charts, such as Hotelling鈥檚 T2 charts, are commonly based on the assumption that the quality characteristics follow a multivariate normal distribution. However, in modern complicated manufacturing systems, appropriate control chart techniques that can efficiently handle the nonnormal processes are required. To overcome the shortcomings of conventional control charts for nonnormal processes, several methods have been proposed to combine statistical learning algorithms and multivariate control charts. Statistical learning-based control charts, such as support vector data description (SVDD)-based charts, k-nearest neighbors-based charts, have proven their improved performance in nonnormal situations compared to that of the T2 chart. Beside the nonnormal property, time-varying operations are also quite common in real manufacturing fields because of various factors such as product and set-point changes, seasonal variations, catalyst degradation, and sensor drifting. However, traditional control charts cannot accommodate future condition changes of the process because they are formulated based on the data information recorded in the early stage of the process. In the present paper, we propose a SVDD algorithm-based control chart, which is capable of adaptively monitoring time-varying and nonnormal processes. We reformulated the SVDD algorithm into a time-adaptive SVDD algorithm by adding a weighting factor that reflects time-varying situations. Moreover, we defined the updating region for the efficient model-updating structure of the control chart. The proposed control chart simultaneously allows efficient model updates and timely detection of out-of-control signals. The effectiveness and applicability of the proposed chart were demonstrated through experiments with the simulated data and the real data from the metal frame process in mobile device manufacturing. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multivariate%20control%20chart" title="multivariate control chart">multivariate control chart</a>, <a href="https://publications.waset.org/abstracts/search?q=nonparametric%20method" title=" nonparametric method"> nonparametric method</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20data%20description" title=" support vector data description"> support vector data description</a>, <a href="https://publications.waset.org/abstracts/search?q=time-varying%20process" title=" time-varying process"> time-varying process</a> </p> <a href="https://publications.waset.org/abstracts/52078/adaptive-process-monitoring-for-time-varying-situations-using-statistical-learning-algorithms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/52078.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">299</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">240</span> Analysis of Diabetes Patients Using Pearson, Cost Optimization, Control Chart Methods</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Devatha%20Kalyan%20Kumar">Devatha Kalyan Kumar</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20Poovarasan"> R. Poovarasan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we have taken certain important factors and health parameters of diabetes patients especially among children by birth (pediatric congenital) where using the above three metrics methods we are going to assess the importance of each attributes in the dataset and thereby determining the most highly responsible and co-related attribute causing diabetics among young patients. We use cost optimization, control chart and Spearmen methodologies for the real-time application of finding the data efficiency in this diabetes dataset. The Spearmen methodology is the correlation methodologies used in software development process to identify the complexity between the various modules of the software. Identifying the complexity is important because if the complexity is higher, then there is a higher chance of occurrence of the risk in the software. With the use of control; chart mean, variance and standard deviation of data are calculated. With the use of Cost optimization model, we find to optimize the variables. Hence we choose the Spearmen, control chart and cost optimization methods to assess the data efficiency in diabetes datasets. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=correlation" title="correlation">correlation</a>, <a href="https://publications.waset.org/abstracts/search?q=congenital%20diabetics" title=" congenital diabetics"> congenital diabetics</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20relationship" title=" linear relationship"> linear relationship</a>, <a href="https://publications.waset.org/abstracts/search?q=monotonic%20function" title=" monotonic function"> monotonic function</a>, <a href="https://publications.waset.org/abstracts/search?q=ranking%20samples" title=" ranking samples"> ranking samples</a>, <a href="https://publications.waset.org/abstracts/search?q=pediatric" title=" pediatric"> pediatric</a> </p> <a href="https://publications.waset.org/abstracts/72132/analysis-of-diabetes-patients-using-pearson-cost-optimization-control-chart-methods" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/72132.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">256</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">239</span> Developing Variable Repetitive Group Sampling Control Chart Using Regression Estimator</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Liaquat%20Ahmad">Liaquat Ahmad</a>, <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Aslam"> Muhammad Aslam</a>, <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Azam"> Muhammad Azam</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this article, we propose a control chart based on repetitive group sampling scheme for the location parameter. This charting scheme is based on the regression estimator; an estimator that capitalize the relationship between the variables of interest to provide more sensitive control than the commonly used individual variables. The control limit coefficients have been estimated for different sample sizes for less and highly correlated variables. The monitoring of the production process is constructed by adopting the procedure of the Shewhart鈥檚 x-bar control chart. Its performance is verified by the average run length calculations when the shift occurs in the average value of the estimator. It has been observed that the less correlated variables have rapid false alarm rate. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=average%20run%20length" title="average run length">average run length</a>, <a href="https://publications.waset.org/abstracts/search?q=control%20charts" title=" control charts"> control charts</a>, <a href="https://publications.waset.org/abstracts/search?q=process%20shift" title=" process shift"> process shift</a>, <a href="https://publications.waset.org/abstracts/search?q=regression%20estimators" title=" regression estimators"> regression estimators</a>, <a href="https://publications.waset.org/abstracts/search?q=repetitive%20group%20sampling" title=" repetitive group sampling"> repetitive group sampling</a> </p> <a href="https://publications.waset.org/abstracts/13539/developing-variable-repetitive-group-sampling-control-chart-using-regression-estimator" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13539.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">565</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">238</span> Creation and Implementation of A New Palliative Care Drug Chart, via A Closed-Loop Audit</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Asfa%20Hussain">Asfa Hussain</a>, <a href="https://publications.waset.org/abstracts/search?q=Chee%20Tang"> Chee Tang</a>, <a href="https://publications.waset.org/abstracts/search?q=Mien%20Nguyen"> Mien Nguyen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Introduction: The safe usage of medications is dependent on clear, well-documented prescribing. Medical drug charts should be regularly checked to ensure that they are fit for purpose. Aims: The purpose of this study was to evaluate whether the Isabel Hospice drug charts were effective or prone to medical errors. The aim was to create a comprehensive palliative care drug chart in line with medico-legal guidelines and to minimise drug administration and prescription errors. Methodology: 50 medical drug charts were audited from March to April 2020, to assess whether they complied with medico-legal guidelines, in a hospice within East of England. Meetings were held with the larger multi-disciplinary team (MDT), including the pharmacists, nursing staff and doctors, to raise awareness of the issue. A preliminary drug chart was created, using the input from the wider MDT. The chart was revised and trialled over 15 times, and each time feedback from the MDT was incorporated into the subsequent template. In the midst of the COVID-19 pandemic in September 2020, the finalised drug chart was trialled. 50 new palliative drug charts were re-audited, to evaluate the changes made. Results: Prescribing and administration errors were high prior to the implementation of the new chart. This improved significantly after introducing the new drug charts, therefore improving patient safety and care. The percentage of inadequately documented allergies went down from 66% to 20% and incorrect oxygen prescription from 40% to 16%. The prescription drug-drug interactions decreased by 30%. Conclusion: It is vital to have clear standardised drug charts, in line with medico-legal standards, to allow ease of prescription and administration of medications and ensure optimum patient-centred care. This closed loop audit demonstrated significant improvement in documentation and prevention of possible fatal drug errors and interactions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=palliative%20care" title="palliative care">palliative care</a>, <a href="https://publications.waset.org/abstracts/search?q=drug%20chart" title=" drug chart"> drug chart</a>, <a href="https://publications.waset.org/abstracts/search?q=medication%20errors" title=" medication errors"> medication errors</a>, <a href="https://publications.waset.org/abstracts/search?q=drug-drug%20interactions" title=" drug-drug interactions"> drug-drug interactions</a>, <a href="https://publications.waset.org/abstracts/search?q=COVID-19" title=" COVID-19"> COVID-19</a>, <a href="https://publications.waset.org/abstracts/search?q=patient%20safety" title=" patient safety"> patient safety</a> </p> <a href="https://publications.waset.org/abstracts/142480/creation-and-implementation-of-a-new-palliative-care-drug-chart-via-a-closed-loop-audit" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/142480.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">176</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">237</span> An AK-Chart for the Non-Normal Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chia-Hau%20Liu">Chia-Hau Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Tai-Yue%20Wang"> Tai-Yue Wang </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Traditional multivariate control charts assume that measurement from manufacturing processes follows a multivariate normal distribution. However, this assumption may not hold or may be difficult to verify because not all the measurement from manufacturing processes are normal distributed in practice. This study develops a new multivariate control chart for monitoring the processes with non-normal data. We propose a mechanism based on integrating the one-class classification method and the adaptive technique. The adaptive technique is used to improve the sensitivity to small shift on one-class classification in statistical process control. In addition, this design provides an easy way to allocate the value of type I error so it is easier to be implemented. Finally, the simulation study and the real data from industry are used to demonstrate the effectiveness of the propose control charts. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multivariate%20control%20chart" title="multivariate control chart">multivariate control chart</a>, <a href="https://publications.waset.org/abstracts/search?q=statistical%20process%20control" title=" statistical process control"> statistical process control</a>, <a href="https://publications.waset.org/abstracts/search?q=one-class%20classification%20method" title=" one-class classification method"> one-class classification method</a>, <a href="https://publications.waset.org/abstracts/search?q=non-normal%20data" title=" non-normal data"> non-normal data</a> </p> <a href="https://publications.waset.org/abstracts/7485/an-ak-chart-for-the-non-normal-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/7485.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">422</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">236</span> The Impact of Artificial Intelligence on Qualty Conrol and Quality</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mary%20Moner%20Botros%20Fanawel">Mary Moner Botros Fanawel</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Many companies use the statistical tool named as statistical quality control, and which can have a high cost for the companies interested on these statistical tools. The evaluation of the quality of products and services is an important topic, but the reduction of the cost of the implantation of the statistical quality control also has important benefits for the companies. For this reason, it is important to implement a economic design for the various steps included into the statistical quality control. In this paper, we describe some relevant aspects related to the economic design of a quality control chart for the proportion of defective items. They are very important because the suggested issues can reduce the cost of implementing a quality control chart for the proportion of defective items. Note that the main purpose of this chart is to evaluate and control the proportion of defective items of a production process. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=model%20predictive%20control" title="model predictive control">model predictive control</a>, <a href="https://publications.waset.org/abstracts/search?q=hierarchical%20control%20structure" title=" hierarchical control structure"> hierarchical control structure</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=water%20quality%20with%20DBPs%20objectives%20proportion" title=" water quality with DBPs objectives proportion"> water quality with DBPs objectives proportion</a>, <a href="https://publications.waset.org/abstracts/search?q=type%20I%20error" title=" type I error"> type I error</a>, <a href="https://publications.waset.org/abstracts/search?q=economic%20plan" title=" economic plan"> economic plan</a>, <a href="https://publications.waset.org/abstracts/search?q=distribution%20function%20bootstrap%20control%20limit" title=" distribution function bootstrap control limit"> distribution function bootstrap control limit</a>, <a href="https://publications.waset.org/abstracts/search?q=p-value%20method" title=" p-value method"> p-value method</a>, <a href="https://publications.waset.org/abstracts/search?q=out-of-control%20signals" title=" out-of-control signals"> out-of-control signals</a>, <a href="https://publications.waset.org/abstracts/search?q=p-value" title=" p-value"> p-value</a>, <a href="https://publications.waset.org/abstracts/search?q=quality%20characteristics" title=" quality characteristics"> quality characteristics</a> </p> <a href="https://publications.waset.org/abstracts/184564/the-impact-of-artificial-intelligence-on-qualty-conrol-and-quality" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/184564.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">62</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">235</span> A Posterior Predictive Model-Based Control Chart for Monitoring Healthcare</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yi-Fan%20Lin">Yi-Fan Lin</a>, <a href="https://publications.waset.org/abstracts/search?q=Peter%20P.%20Howley"> Peter P. Howley</a>, <a href="https://publications.waset.org/abstracts/search?q=Frank%20A.%20Tuyl"> Frank A. Tuyl</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Quality measurement and reporting systems are used in healthcare internationally. In Australia, the Australian Council on Healthcare Standards records and reports hundreds of clinical indicators (CIs) nationally across the healthcare system. These CIs are measures of performance in the clinical setting, and are used as a screening tool to help assess whether a standard of care is being met. Existing analysis and reporting of these CIs incorporate Bayesian methods to address sampling variation; however, such assessments are retrospective in nature, reporting upon the previous six or twelve months of data. The use of Bayesian methods within statistical process control for monitoring systems is an important pursuit to support more timely decision-making. Our research has developed and assessed a new graphical monitoring tool, similar to a control chart, based on the beta-binomial posterior predictive (BBPP) distribution to facilitate the real-time assessment of health care organizational performance via CIs. The BBPP charts have been compared with the traditional Bernoulli CUSUM (BC) chart by simulation. The more traditional “central” and “highest posterior density” (HPD) interval approaches were each considered to define the limits, and the multiple charts were compared via in-control and out-of-control average run lengths (ARLs), assuming that the parameter representing the underlying CI rate (proportion of cases with an event of interest) required estimation. Preliminary results have identified that the BBPP chart with HPD-based control limits provides better out-of-control run length performance than the central interval-based and BC charts. Further, the BC chart’s performance may be improved by using Bayesian parameter estimation of the underlying CI rate. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=average%20run%20length%20%28ARL%29" title="average run length (ARL)">average run length (ARL)</a>, <a href="https://publications.waset.org/abstracts/search?q=bernoulli%20cusum%20%28BC%29%20chart" title=" bernoulli cusum (BC) chart"> bernoulli cusum (BC) chart</a>, <a href="https://publications.waset.org/abstracts/search?q=beta%20binomial%20posterior%20predictive%20%28BBPP%29%20distribution" title=" beta binomial posterior predictive (BBPP) distribution"> beta binomial posterior predictive (BBPP) distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=clinical%20indicator%20%28CI%29" title=" clinical indicator (CI)"> clinical indicator (CI)</a>, <a href="https://publications.waset.org/abstracts/search?q=healthcare%20organization%20%28HCO%29" title=" healthcare organization (HCO)"> healthcare organization (HCO)</a>, <a href="https://publications.waset.org/abstracts/search?q=highest%20posterior%20density%20%28HPD%29%20interval" title=" highest posterior density (HPD) interval"> highest posterior density (HPD) interval</a> </p> <a href="https://publications.waset.org/abstracts/95410/a-posterior-predictive-model-based-control-chart-for-monitoring-healthcare" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/95410.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">201</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">234</span> Establishing Control Chart Limits for Rounded Measurements</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ran%20Etgar">Ran Etgar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The process of rounding off measurements in continuous variables is commonly encountered. Although it usually has minor effects, sometimes it can lead to poor outcomes in statistical process control using X虅 chart. The traditional control limits can cause incorrect conclusions if applied carelessly. This study looks into the limitations of classical control limits, particularly the impact of asymmetry. An approach to determining the distribution function of the measured parameter 瘸 is presented, resulting in a more precise method to establish the upper and lower control limits. The proposed method, while slightly more complex than Shewhart's original idea, is still user-friendly and accurate and only requires the use of two straightforward tables. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=SPC" title="SPC">SPC</a>, <a href="https://publications.waset.org/abstracts/search?q=round-off%20data" title=" round-off data"> round-off data</a>, <a href="https://publications.waset.org/abstracts/search?q=control%20limit" title=" control limit"> control limit</a>, <a href="https://publications.waset.org/abstracts/search?q=rounding%20error" title=" rounding error"> rounding error</a> </p> <a href="https://publications.waset.org/abstracts/162235/establishing-control-chart-limits-for-rounded-measurements" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/162235.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">75</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">233</span> Rounded-off Measurements and Their Implication on Control Charts</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ran%20Etgar">Ran Etgar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The process of rounding off measurements in continuous variables is commonly encountered. Although it usually has minor effects, sometimes it can lead to poor outcomes in statistical process control using X 虆-chart. The traditional control limits can cause incorrect conclusions if applied carelessly. This study looks into the limitations of classical control limits, particularly the impact of asymmetry. An approach to determining the distribution function of the measured parameter (Y 虆) is presented, resulting in a more precise method to establish the upper and lower control limits. The proposed method, while slightly more complex than Shewhart's original idea, is still user-friendly and accurate and only requires the use of two straightforward tables. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=inaccurate%20measurement" title="inaccurate measurement">inaccurate measurement</a>, <a href="https://publications.waset.org/abstracts/search?q=SPC" title=" SPC"> SPC</a>, <a href="https://publications.waset.org/abstracts/search?q=statistical%20process%20control" title=" statistical process control"> statistical process control</a>, <a href="https://publications.waset.org/abstracts/search?q=rounded-off" title=" rounded-off"> rounded-off</a>, <a href="https://publications.waset.org/abstracts/search?q=control%20chart" title=" control chart"> control chart</a> </p> <a href="https://publications.waset.org/abstracts/188545/rounded-off-measurements-and-their-implication-on-control-charts" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/188545.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">40</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">232</span> Controlling the Process of a Chicken Dressing Plant through Statistical Process Control</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jasper%20Kevin%20C.%20Dionisio">Jasper Kevin C. Dionisio</a>, <a href="https://publications.waset.org/abstracts/search?q=Denise%20Mae%20M.%20Unsay"> Denise Mae M. Unsay</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In a manufacturing firm, controlling the process ensures that optimum efficiency, productivity, and quality in an organization are achieved. An operation with no standardized procedure yields a poor productivity, inefficiency, and an out of control process. This study focuses on controlling the small intestine processing of a chicken dressing plant through the use of Statistical Process Control (SPC). Since the operation does not employ a standard procedure and does not have an established standard time, the process through the assessment of the observed time of the overall operation of small intestine processing, through the use of X-Bar R Control Chart, is found to be out of control. In the solution of this problem, the researchers conduct a motion and time study aiming to establish a standard procedure for the operation. The normal operator was picked through the use of Westinghouse Rating System. Instead of utilizing the traditional motion and time study, the researchers used the X-Bar R Control Chart in determining the process average of the process that is used for establishing the standard time. The observed time of the normal operator was noted and plotted to the X-Bar R Control Chart. Out of control points that are due to assignable cause were removed and the process average, or the average time the normal operator conducted the process, which was already in control and free form any outliers, was obtained. The process average was then used in determining the standard time of small intestine processing. As a recommendation, the researchers suggest the implementation of the standard time established which is with consonance to the standard procedure which was adopted from the normal operator. With that recommendation, the whole operation will induce a 45.54 % increase in their productivity. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=motion%20and%20time%20study" title="motion and time study">motion and time study</a>, <a href="https://publications.waset.org/abstracts/search?q=process%20controlling" title=" process controlling"> process controlling</a>, <a href="https://publications.waset.org/abstracts/search?q=statistical%20process%20control" title=" statistical process control"> statistical process control</a>, <a href="https://publications.waset.org/abstracts/search?q=X-Bar%20R%20Control%20chart" title=" X-Bar R Control chart"> X-Bar R Control chart</a> </p> <a href="https://publications.waset.org/abstracts/78980/controlling-the-process-of-a-chicken-dressing-plant-through-statistical-process-control" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/78980.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">217</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">231</span> Pattern Identification in Statistical Process Control Using Artificial Neural Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Pramila%20Devi">M. Pramila Devi</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20V.%20N.%20Indra%20Kiran"> N. V. N. Indra Kiran</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Control charts, predominantly in the form of X-bar chart, are important tools in statistical process control (SPC). They are useful in determining whether a process is behaving as intended or there are some unnatural causes of variation. A process is out of control if a point falls outside the control limits or a series of point鈥檚 exhibit an unnatural pattern. In this paper, a study is carried out on four training algorithms for CCPs recognition. For those algorithms optimal structure is identified and then they are studied for type I and type II errors for generalization without early stopping and with early stopping and the best one is proposed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=control%20chart%20pattern%20recognition" title="control chart pattern recognition">control chart pattern recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title=" neural network"> neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=backpropagation" title=" backpropagation"> backpropagation</a>, <a href="https://publications.waset.org/abstracts/search?q=generalization" title=" generalization"> generalization</a>, <a href="https://publications.waset.org/abstracts/search?q=early%20stopping" title=" early stopping"> early stopping</a> </p> <a href="https://publications.waset.org/abstracts/6307/pattern-identification-in-statistical-process-control-using-artificial-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/6307.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">372</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">230</span> Using Deep Learning Neural Networks and Candlestick Chart Representation to Predict Stock Market</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rosdyana%20Mangir%20Irawan%20Kusuma">Rosdyana Mangir Irawan Kusuma</a>, <a href="https://publications.waset.org/abstracts/search?q=Wei-Chun%20Kao"> Wei-Chun Kao</a>, <a href="https://publications.waset.org/abstracts/search?q=Ho-Thi%20Trang"> Ho-Thi Trang</a>, <a href="https://publications.waset.org/abstracts/search?q=Yu-Yen%20Ou"> Yu-Yen Ou</a>, <a href="https://publications.waset.org/abstracts/search?q=Kai-Lung%20Hua"> Kai-Lung Hua</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Stock market prediction is still a challenging problem because there are many factors that affect the stock market price such as company news and performance, industry performance, investor sentiment, social media sentiment, and economic factors. This work explores the predictability in the stock market using deep convolutional network and candlestick charts. The outcome is utilized to design a decision support framework that can be used by traders to provide suggested indications of future stock price direction. We perform this work using various types of neural networks like convolutional neural network, residual network and visual geometry group network. From stock market historical data, we converted it to candlestick charts. Finally, these candlestick charts will be feed as input for training a convolutional neural network model. This convolutional neural network model will help us to analyze the patterns inside the candlestick chart and predict the future movements of the stock market. The effectiveness of our method is evaluated in stock market prediction with promising results; 92.2% and 92.1 % accuracy for Taiwan and Indonesian stock market dataset respectively. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=candlestick%20chart" title="candlestick chart">candlestick chart</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title=" neural network"> neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=stock%20market%20prediction" title=" stock market prediction"> stock market prediction</a> </p> <a href="https://publications.waset.org/abstracts/98615/using-deep-learning-neural-networks-and-candlestick-chart-representation-to-predict-stock-market" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/98615.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">447</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">229</span> Design of a Compact Microstrip Patch Antenna for LTE Applications by Applying FDSC Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Settapong%20Malisuwan">Settapong Malisuwan</a>, <a href="https://publications.waset.org/abstracts/search?q=Jesada%20Sivaraks"> Jesada Sivaraks</a>, <a href="https://publications.waset.org/abstracts/search?q=Peerawat%20Promkladpanao"> Peerawat Promkladpanao</a>, <a href="https://publications.waset.org/abstracts/search?q=Nattakit%20Suriyakrai"> Nattakit Suriyakrai</a>, <a href="https://publications.waset.org/abstracts/search?q=Navneet%20Madan"> Navneet Madan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a compact microstrip patch antenna is designed for mobile LTE applications by applying the frequency-dependent Smith-Chart (FDSC) model. The FDSC model is adopted in this research to reduce the error on the frequency-dependent characteristics. The Ansoft HFSS and various techniques is applied to meet frequency and size requirements. The proposed method within this research is suitable for use in computer-aided microstrip antenna design and RF integrated circuit (RFIC) design. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=frequency-dependent" title="frequency-dependent">frequency-dependent</a>, <a href="https://publications.waset.org/abstracts/search?q=smith-chart" title=" smith-chart"> smith-chart</a>, <a href="https://publications.waset.org/abstracts/search?q=microstrip" title=" microstrip"> microstrip</a>, <a href="https://publications.waset.org/abstracts/search?q=antenna" title=" antenna"> antenna</a>, <a href="https://publications.waset.org/abstracts/search?q=LTE" title=" LTE"> LTE</a>, <a href="https://publications.waset.org/abstracts/search?q=CAD" title=" CAD"> CAD</a> </p> <a href="https://publications.waset.org/abstracts/4229/design-of-a-compact-microstrip-patch-antenna-for-lte-applications-by-applying-fdsc-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/4229.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">374</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">228</span> Multivariate Control Chart to Determine Efficiency Measurements in Industrial Processes</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=J.%20J.%20Vargas">J. J. Vargas</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20Prieto"> N. Prieto</a>, <a href="https://publications.waset.org/abstracts/search?q=L.%20A.%20Toro"> L. A. Toro</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Control charts are commonly used to monitor processes involving either variable or attribute of quality characteristics and determining the control limits as a critical task for quality engineers to improve the processes. Nonetheless, in some applications it is necessary to include an estimation of efficiency. In this paper, the ability to define the efficiency of an industrial process was added to a control chart by means of incorporating a data envelopment analysis (DEA) approach. In depth, a Bayesian estimation was performed to calculate the posterior probability distribution of parameters as means and variance and covariance matrix. This technique allows to analyse the data set without the need of using the hypothetical large sample implied in the problem and to be treated as an approximation to the finite sample distribution. A rejection simulation method was carried out to generate random variables from the parameter functions. Each resulting vector was used by stochastic DEA model during several cycles for establishing the distribution of each efficiency measures for each DMU (decision making units). A control limit was calculated with model obtained and if a condition of a low level efficiency of DMU is presented, system efficiency is out of control. In the efficiency calculated a global optimum was reached, which ensures model reliability. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20envelopment%20analysis" title="data envelopment analysis">data envelopment analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=DEA" title=" DEA"> DEA</a>, <a href="https://publications.waset.org/abstracts/search?q=Multivariate%20control%20chart" title=" Multivariate control chart"> Multivariate control chart</a>, <a href="https://publications.waset.org/abstracts/search?q=rejection%20simulation%20method" title=" rejection simulation method"> rejection simulation method</a> </p> <a href="https://publications.waset.org/abstracts/37123/multivariate-control-chart-to-determine-efficiency-measurements-in-industrial-processes" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/37123.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">374</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">227</span> Modified CUSUM Algorithm for Gradual Change Detection in a Time Series Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Victoria%20Siriaki%20Jorry">Victoria Siriaki Jorry</a>, <a href="https://publications.waset.org/abstracts/search?q=I.%20S.%20Mbalawata"> I. S. Mbalawata</a>, <a href="https://publications.waset.org/abstracts/search?q=Hayong%20Shin"> Hayong Shin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The main objective in a change detection problem is to develop algorithms for efficient detection of gradual and/or abrupt changes in the parameter distribution of a process or time series data. In this paper, we present a modified cumulative (MCUSUM) algorithm to detect the start and end of a time-varying linear drift in mean value of a time series data based on likelihood ratio test procedure. The design, implementation and performance of the proposed algorithm for a linear drift detection is evaluated and compared to the existing CUSUM algorithm using different performance measures. An approach to accurately approximate the threshold of the MCUSUM is also provided. Performance of the MCUSUM for gradual change-point detection is compared to that of standard cumulative sum (CUSUM) control chart designed for abrupt shift detection using Monte Carlo Simulations. In terms of the expected time for detection, the MCUSUM procedure is found to have a better performance than a standard CUSUM chart for detection of the gradual change in mean. The algorithm is then applied and tested to a randomly generated time series data with a gradual linear trend in mean to demonstrate its usefulness. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=average%20run%20length" title="average run length">average run length</a>, <a href="https://publications.waset.org/abstracts/search?q=CUSUM%20control%20chart" title=" CUSUM control chart"> CUSUM control chart</a>, <a href="https://publications.waset.org/abstracts/search?q=gradual%20change%20detection" title=" gradual change detection"> gradual change detection</a>, <a href="https://publications.waset.org/abstracts/search?q=likelihood%20ratio%20test" title=" likelihood ratio test"> likelihood ratio test</a> </p> <a href="https://publications.waset.org/abstracts/70339/modified-cusum-algorithm-for-gradual-change-detection-in-a-time-series-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/70339.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">299</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">226</span> GRCNN: Graph Recognition Convolutional Neural Network for Synthesizing Programs from Flow Charts</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lin%20Cheng">Lin Cheng</a>, <a href="https://publications.waset.org/abstracts/search?q=Zijiang%20Yang"> Zijiang Yang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Program synthesis is the task to automatically generate programs based on user specification. In this paper, we present a framework that synthesizes programs from flow charts that serve as accurate and intuitive specification. In order doing so, we propose a deep neural network called GRCNN that recognizes graph structure from its image. GRCNN is trained end-to-end, which can predict edge and node information of the flow chart simultaneously. Experiments show that the accuracy rate to synthesize a program is 66.4%, and the accuracy rates to recognize edge and node are 94.1% and 67.9%, respectively. On average, it takes about 60 milliseconds to synthesize a program. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=program%20synthesis" title="program synthesis">program synthesis</a>, <a href="https://publications.waset.org/abstracts/search?q=flow%20chart" title=" flow chart"> flow chart</a>, <a href="https://publications.waset.org/abstracts/search?q=specification" title=" specification"> specification</a>, <a href="https://publications.waset.org/abstracts/search?q=graph%20recognition" title=" graph recognition"> graph recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=CNN" title=" CNN"> CNN</a> </p> <a href="https://publications.waset.org/abstracts/124641/grcnn-graph-recognition-convolutional-neural-network-for-synthesizing-programs-from-flow-charts" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/124641.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">119</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=ASM%20chart&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=ASM%20chart&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=ASM%20chart&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=ASM%20chart&page=5">5</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=ASM%20chart&page=6">6</a></li> <li class="page-item"><a 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